[Docs]Add recipe for GLM-Image on 2x A800 GPUs and 1x A800 GPU

recipes/GLM/GLM-Image.md

@@ -0,0 +1,112 @@
+# GLM-Image for text-to-image and image editing
+
+## Summary
+
+- Vendor: Z.ai
+- Model: `GLM/GLM-Image`
+- Task: Text-to-image (T2I) and image-to-image
+- Mode: Online serving with the OpenAI-compatible API
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want a known-good starting point for serving
+`GLM/GLM-Image` with vLLM-Omni on **two 80 GB NVIDIA A800** GPUs (Ampere-class,
+same default layout as the upstream **2×A100 80GB** example: Stage 0 AR on GPU 0,
+Stage 1 diffusion on GPU 1) and validate the deployment with the existing
+`examples/online_serving/glm_image` clients.
+
+## References
+
+- Upstream or canonical docs:
+  [`docs/user_guide/examples/online_serving/glm_image.md`](../../docs/user_guide/examples/online_serving/glm_image.md)
+- Related issue or discussion:
+  [#2888](https://github.com/vllm-project/vllm-omni/pull/2888)
+
+## Hardware Support
+
+This recipe documents **dual-GPU** CUDA layouts on A800 80 GB
+for the same software stack. Add more platforms (for example ROCm / NPU) as
+community validation lands.
+
+## GPU
+
+### 2× A800 80GB
+
+#### Environment
+
+These versions were taken from a working **editable** install: activate `vllm-omni/.venv` (or your equivalent), then align `pip` / Git with the rows below when reproducing this recipe.
+
+- OS: Linux
+- Python: 3.12
+- Driver / runtime: NVIDIA CUDA stack with **two** A800 80 GB GPUs visible (set `CUDA_VISIBLE_DEVICES` on your host if needed)
+- vLLM: **0.19.0**
+- vLLM-Omni: **0.19.0rc2.dev138+g38d5f2d53** (editable install from this repo; Git **`38d5f2d5`**, `git describe` ≈ **`v0.19.0rc1-138-g38d5f2d5`**)
+- Transformers: **5.5.4** (same `.venv` as above; required so `glm_image` configs load for Stage 0)
+
+#### Command
+
+Start the server from the repository root:
+
+```bash
+vllm serve zai-org/GLM-Image --omni --port 8091
+```
+
+To use the bundled stage config explicitly (same default as above):
+
+```bash
+vllm serve zai-org/GLM-Image \
+  --omni \
+  --port 8091 \
+  --deploy-config vllm_omni/deploy/glm_image.yaml
+```
+
+#### Verification
+
+Run one of the existing example clients after the server is ready:
+
+```bash
+curl -s http://172.18.69.133:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": "A beautiful landscape painting"}
+    ],
+    "extra_body": {
+      "height": 1920,
+      "width": 1920,
+      "num_inference_steps": 50,
+      "true_cfg_scale": 1.5,
+      "seed": 42
+    }
+  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > land.png
+```
+After the command finishes, check for the output files:
+
+```bash
+ls output.png
+```
+
+#### Sample end-to-end metrics
+
+One representative **offline** GLM-Image E2E run on this recipe’s **2× A800 80GB**.
+Overall summary from the run’s metrics. Rough wall-time split: **Stage 0 (AR)** ~**25 s**,
+**Stage 1 (diffusion)** ~**34 s** (see `e2e_stage_*_wall_time_ms` below).
+
+| Field | Value |
+| --- | ---: |
+| e2e_requests | 1 |
+| e2e_wall_time_ms | 61,148.679 |
+| e2e_total_tokens | 1,300 |
+| e2e_avg_time_per_request_ms | 61,148.679 |
+| e2e_avg_tokens_per_s | 21.260 |
+| e2e_stage_0_wall_time_ms | 24,708.760 |
+| e2e_stage_1_wall_time_ms | 33,787.442 |
+
+#### Notes
+
+- Memory usage: Roughly **~38 GiB + KV** on Stage 0 (AR) and **~20 GiB** on Stage 1 (DiT+VAE) per the user guide; two 80 GB cards match the default split.
+- Key flags: `--omni` is required; `--stage-configs-path` is optional unless you use a custom YAML (for example single-GPU).
+- Keep **Transformers ≥ 5.5.1** (this recipe used **5.5.4**) so `glm_image` configs resolve; otherwise Stage 0 can fail at `ModelConfig` validation.
+- Known limitations: This starter recipe follows the dual-GPU online path documented under `examples/online_serving/glm_image`. The first request may be slower due to warmup.
+- Generation time: about **61 s** wall time end-to-end for the sample above (50 inference steps, 1024×1024).

recipes/README.md

@@ -30,6 +30,9 @@ recipes/
 - [`Wan-AI/Wan2.2-I2V.md`](./Wan-AI/Wan2.2-I2V.md): image-to-video serving
   recipe for Wan2.2 14B on `8x Ascend NPU (A2/A3)`
 
+- [`GLM/GLM-Image.md`](./GLM/GLM-Image.md):online serving recipe for
+  image generation on `1x A800 80GB` and `2x A800 80GB`
+
 Within a single recipe file, include different hardware support sections such
 as `GPU`, `ROCm`, and `NPU`, and add concrete tested configurations like
 `1x A100 80GB` or `2x L40S` inside those sections when applicable.